Video surveillance and analytics systems may employ a background model (for example simple or complex) to distinguish “interesting” or notable changes in image frames originating in a live video stream or stored/archived video sequence (e.g., movie), from “non-interesting” static or quasi-static backgrounds. An interesting change can be any change that the operator wants to notice or be notified of—e.g., a person, or a person engaging in suspicious activity, or a suspicious object, or a target person or object, or a known person or object. An uninteresting change can be any change that the operator does not care to notice or be notified of—e.g., weather, animals, changes in lighting, natural phenomenon, etc. For example, a video surveillance system may use a reference image (supplied by the system's background model module) of a train station, and compare it with a video image of the train station. Differences between the video image and the reference image may indicate a suspicious situation, a disturbance in the train station or trespassing/intrusion, for example. The detected changes may be processed and analyzed to determine the types of change detected and whether an alert or other action is necessary. In general, the goals of an automated video surveillance system may be to maximize the probability of detecting changes that require alerts while also minimizing the probability of false alarms (e.g., spurious and non-desired alerts).